On a slight tangent, I feel like this is one major ace up Apple’s sleeve, if they can zoom into it. With the awesome performance of Apple Silicon and how lots of the big leaps are seen with video rendering, they just need to focus some more on the ML capabilities. They’ve been skirting around ML with some optimizing on popular ML libraries, but it’s mostly focused on inference, but hopefully with recent pytorch 2.0 optimizations and co, they can meet open source libraries halfway, and do more.
I think the current proliferation of AI and general awareness of LLM can be a major selling point if they make sure their neural engine is well optimized for it. Will put them right at the center of the conversation, especially since one of the current concern is the cost of training these models.
As an academic researcher with a significant interest and time-investment in Transformer-based models, this restores my faith/hope in the trajectory of DL research. Considering it is difficult for academics to catch up to the industry regarding LLMs, seeing a continuation of the OPENness of these research works by a major industry player is a move in the right direction.
I think the current proliferation of AI and general awareness of LLM can be a major selling point if they make sure their neural engine is well optimized for it. Will put them right at the center of the conversation, especially since one of the current concern is the cost of training these models.